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\documentclass[11pt]{article}
\usepackage{acl2014}
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\title{Multi-Document Summarization using Dependency Grammar Based Similarity Kernels}

\author{Saziye Betul Bilgin \\
  Bogazici University,  \\
  Computer Engineering Dept. \\
  {\tt saziye.bilgin@boun.edu.tr} \\\And
  Hakime Ozturk\\
  Bogazici University,  \\
  Computer Engineering Dept. \\
  {\tt hakime.ozturk@boun.edu.tr} \\}

\date{}

\begin{document}
\maketitle
\begin{abstract}
{\fontsize{10pt}{1em}\selectfont
    In this study, we introduce the usage of dependency tree based kernels for comptuning sentence similarity in multi-document summarization. We adapt two different dependency tree based similarity kernels and design a new method based on typed dependency grammars for sentence similarity computation. These three methods are tested on LexRank summarization system. We compare each of their performances with the baseline Lead-based approach and original LexRank setup. All of the dependency based sentence similarity methods have failed to achieve higher score than LexRank with tf-idf based cosine similarity method. Typed Dependency Similarity Function gives the best performance when compared to other dependency based kernels and the baseline approach. 
}
\end{abstract}


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